Energy consumption is a critical concern worldwide due to its impact on the environment, economy, and human welfare. Therefore, understanding the factors that influence energy consumption in buildings is essential to optimize energy use and minimize its negative effects. Multiple linear regression is a statistical method used to model the relationship between a dependent variable and several independent variables simultaneously. In this report, we perform a multiple linear regression analysis to investigate the factors that affect energy consumption. The analysis is based on a dataset that includes information on natural gas consumption and several variables related to weather conditions (such as the mean external temperature and the irradiance). The objective of this study is to identify the significant predictors of energy consumption and provide insights into the underlying mechanisms that drive energy use.
The dataset utilized in this analysis is composed by 3 numerical variables, total daily gas consumption Energy \([Smc]\), mean daily external temperature Text \([°C]\), and mean solar irradiance Iext \([W/m^2]\) and 1 categorical variable, the day of the week DayofWeek.
The dataset provides daily measurements of these variables for a full heating season in Turin, which goes from \(1^{st}\) November to \(31^{th}\) March, resulting in a total of 151 records.
In the table below is reported a sketch of the dataset.
The trend of the variables during the heating season is represented in the figure below.